Social media analytics

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Learn how data science can help derive insight from social media content by first defining different types of social media data and their nature, such as raw data vs. metadata. Explore how various data science technologies, such as text mining and parsing

- [Voiceover] More and more people are using social media.…This in turn generates an enormous amount of data.…Data scientists are naturally attracted to…these newly emerging types of data sets.…Social media refers to websites…where users can post their own content…to share it with their friends and beyond.…Depending on their focus, social media sites have…different types of interests they promote.…For example, Facebook offers a forum…for building informal and personal relationships,…compared to a professional networking tool like LinkedIn.…

In addition to its size qualifying as big data,…another unique value of social media data…lies in the data about data, or metadata, it carries.…For example, a post on Facebook can accompany…location information as well as timestamps.…With this kind of unstructured but very rich data sets,…a lot of useful insight can be derived about a person…who is posting and consuming information.…

For example, IBM has a product called Personality Insights…which offers a profiling service for companies…

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Released

1/26/2018

The career opportunities in data science, big data, and data analytics are growing dramatically. If you're interested in changing career paths, determining the right course of study, or deciding if certification is worth your time, this course is for you.

Jungwoo Ryoo is a professor of information science and technology at Penn State. Here he reviews the history of data science and its subfields, explores the marketplaces for these fields, and reveals the five main skills areas: data mining, machine learning, natural language processing (NLP), statistics, and visualization. This leads to a discussion of the five biggest career opportunities, the six leading industry-recognized certifications available, and the most exciting emerging technologies. Along the way, Jungwoo discusses the importance of ethics and professional development, and provides pointers to online resources for learning more.

Topics include:

A history of data science

Why data analytics is important

How data science is used in fraud detection, disease control, network security, and other fields